Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
Authors: Akifumi Okuno, Hidetoshi Shimodaira
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,051 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,245 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically prove that the MS-k-NN attains the improved rate, which coincides with the existing optimal rate under some conditions. Numerical experiments are conducted for performing MS-k-NN. |
| Researcher Affiliation | Collaboration | Akifumi Okuno1,3 and Hidetoshi Shimodaira2,3 1School of Statistical Thinking, The Institute of Statistical Mathematics 2Graduate School of Informatics, Kyoto University 3RIKEN Center for Advanced Intelligence Project |
| Pseudocode | No | The paper describes a minimization problem formally: 'ˆb := arg min b RC+1 ˆη(k NN) n,kv (X ) b0 c=1 bcr2c v' but this is a mathematical expression, not a pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We employ datasets from UCI Machine Learning Repository (Dua & Graff, 2017). |
| Dataset Splits | Yes | Feature vectors are first normalized, and then randomly divided into 70% for prediction (npred = 0.7n ) and the remaining for test query. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., CPU, GPU models, or cloud computing instances). |
| Software Dependencies | No | The paper does not mention specific software names with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | Parameter tuning: For unweighted and weighted k-NN, we first fix k := V n4/(4+d) pred n4/(4+d) pred . Using the same k, we simply choose k1 := k/V, k2 = 2k/V, . . . , k V = k with V = 5 for MS-k-NN. Regression in MS-k-NN is ridge regularized with the coefficient λ = 10 4. |